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Engineering with Computers

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01.10.2016 | Original Article

Extreme learning machine assessment for estimating sediment transport in open channels

verfasst von: Isa Ebtehaj, Hossein Bonakdari, Shahaboddin Shamshirband

Erschienen in: Engineering with Computers | Ausgabe 4/2016

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Abstract

The minimum velocity required to prevent sediment deposition in open channels is examined in this study. The parameters affecting transport are first determined and then categorized into different dimensionless groups, including “movement,” “transport,” “sediment,” “transport mode,” and “flow resistance.” Six different models are presented to identify the effect of each of these parameters. The feed-forward neural network (FFNN) is used to predict the densimetric Froude number (Fr) and the extreme learning machine (ELM) algorithm is utilized to train it. The results of this algorithm are compared with back propagation (BP), genetic programming (GP) and existing sediment transport equations. The results indicate that FFNN-ELM produced better results than FNN-BP, GP and existing sediment transport methods in both training (RMSE = 0.26 and MARE = 0.052) and testing (RMSE = 0.121 and MARE = 0.023). Moreover, the performance of FFNN-ELM is examined for different pipe diameters.

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Titel: Extreme learning machine assessment for estimating sediment transport in open channels
verfasst von: Isa Ebtehaj
Hossein Bonakdari
Shahaboddin Shamshirband
Publikationsdatum: 01.10.2016
Verlag: Springer London
Erschienen in: Engineering with Computers / Ausgabe 4/2016
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-016-0446-1

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